Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations1197
Missing cells506
Missing cells (%)2.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory406.3 KiB
Average record size in memory347.5 B

Variable types

DateTime1
Categorical4
Numeric10

Alerts

department is highly overall correlated with no_of_workers and 3 other fieldsHigh correlation
idle_men is highly overall correlated with idle_timeHigh correlation
idle_time is highly overall correlated with idle_menHigh correlation
incentive is highly overall correlated with no_of_workers and 2 other fieldsHigh correlation
no_of_workers is highly overall correlated with department and 3 other fieldsHigh correlation
over_time is highly overall correlated with department and 3 other fieldsHigh correlation
smv is highly overall correlated with department and 3 other fieldsHigh correlation
wip is highly overall correlated with departmentHigh correlation
no_of_style_change is highly imbalanced (60.1%)Imbalance
wip has 506 (42.3%) missing valuesMissing
idle_time is highly skewed (γ1 = 20.54542523)Skewed
over_time has 31 (2.6%) zerosZeros
incentive has 604 (50.5%) zerosZeros
idle_time has 1179 (98.5%) zerosZeros
idle_men has 1179 (98.5%) zerosZeros

Reproduction

Analysis started2025-10-24 17:22:39.530098
Analysis finished2025-10-24 17:22:51.854911
Duration12.32 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

date
Date

Distinct59
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Minimum2015-01-01 00:00:00
Maximum2015-03-11 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-24T12:22:51.986847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:52.154964image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

quarter
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
Quarter1
360 
Quarter2
335 
Quarter4
248 
Quarter3
210 
Quarter5
44 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters9576
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQuarter1
2nd rowQuarter1
3rd rowQuarter1
4th rowQuarter1
5th rowQuarter1

Common Values

ValueCountFrequency (%)
Quarter1360
30.1%
Quarter2335
28.0%
Quarter4248
20.7%
Quarter3210
17.5%
Quarter544
 
3.7%

Length

2025-10-24T12:22:52.305483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-24T12:22:52.434542image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
quarter1360
30.1%
quarter2335
28.0%
quarter4248
20.7%
quarter3210
17.5%
quarter544
 
3.7%

Most occurring characters

ValueCountFrequency (%)
r2394
25.0%
Q1197
12.5%
u1197
12.5%
a1197
12.5%
t1197
12.5%
e1197
12.5%
1360
 
3.8%
2335
 
3.5%
4248
 
2.6%
3210
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)9576
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r2394
25.0%
Q1197
12.5%
u1197
12.5%
a1197
12.5%
t1197
12.5%
e1197
12.5%
1360
 
3.8%
2335
 
3.5%
4248
 
2.6%
3210
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9576
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r2394
25.0%
Q1197
12.5%
u1197
12.5%
a1197
12.5%
t1197
12.5%
e1197
12.5%
1360
 
3.8%
2335
 
3.5%
4248
 
2.6%
3210
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9576
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r2394
25.0%
Q1197
12.5%
u1197
12.5%
a1197
12.5%
t1197
12.5%
e1197
12.5%
1360
 
3.8%
2335
 
3.5%
4248
 
2.6%
3210
 
2.2%

department
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size75.5 KiB
sweing
691 
finishing
257 
finishing
249 

Length

Max length10
Median length6
Mean length7.4828739
Min length6

Characters and Unicode

Total characters8957
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsweing
2nd rowfinishing
3rd rowsweing
4th rowsweing
5th rowsweing

Common Values

ValueCountFrequency (%)
sweing691
57.7%
finishing257
 
21.5%
finishing249
 
20.8%

Length

2025-10-24T12:22:52.594308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-24T12:22:52.715750image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
sweing691
57.7%
finishing506
42.3%

Most occurring characters

ValueCountFrequency (%)
i2209
24.7%
n1703
19.0%
s1197
13.4%
g1197
13.4%
w691
 
7.7%
e691
 
7.7%
f506
 
5.6%
h506
 
5.6%
257
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)8957
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i2209
24.7%
n1703
19.0%
s1197
13.4%
g1197
13.4%
w691
 
7.7%
e691
 
7.7%
f506
 
5.6%
h506
 
5.6%
257
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8957
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i2209
24.7%
n1703
19.0%
s1197
13.4%
g1197
13.4%
w691
 
7.7%
e691
 
7.7%
f506
 
5.6%
h506
 
5.6%
257
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8957
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i2209
24.7%
n1703
19.0%
s1197
13.4%
g1197
13.4%
w691
 
7.7%
e691
 
7.7%
f506
 
5.6%
h506
 
5.6%
257
 
2.9%

day
Categorical

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size75.3 KiB
Wednesday
208 
Sunday
203 
Tuesday
201 
Thursday
199 
Monday
199 

Length

Max length9
Median length8
Mean length7.3341688
Min length6

Characters and Unicode

Total characters8779
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThursday
2nd rowThursday
3rd rowThursday
4th rowThursday
5th rowThursday

Common Values

ValueCountFrequency (%)
Wednesday208
17.4%
Sunday203
17.0%
Tuesday201
16.8%
Thursday199
16.6%
Monday199
16.6%
Saturday187
15.6%

Length

2025-10-24T12:22:52.843077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-24T12:22:53.008964image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
wednesday208
17.4%
sunday203
17.0%
tuesday201
16.8%
thursday199
16.6%
monday199
16.6%
saturday187
15.6%

Most occurring characters

ValueCountFrequency (%)
d1405
16.0%
a1384
15.8%
y1197
13.6%
u790
9.0%
e617
7.0%
n610
6.9%
s608
6.9%
T400
 
4.6%
S390
 
4.4%
r386
 
4.4%
Other values (5)992
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)8779
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d1405
16.0%
a1384
15.8%
y1197
13.6%
u790
9.0%
e617
7.0%
n610
6.9%
s608
6.9%
T400
 
4.6%
S390
 
4.4%
r386
 
4.4%
Other values (5)992
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8779
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d1405
16.0%
a1384
15.8%
y1197
13.6%
u790
9.0%
e617
7.0%
n610
6.9%
s608
6.9%
T400
 
4.6%
S390
 
4.4%
r386
 
4.4%
Other values (5)992
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8779
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d1405
16.0%
a1384
15.8%
y1197
13.6%
u790
9.0%
e617
7.0%
n610
6.9%
s608
6.9%
T400
 
4.6%
S390
 
4.4%
r386
 
4.4%
Other values (5)992
11.3%

team
Real number (ℝ)

Distinct12
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4269006
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-10-24T12:22:53.186979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4639633
Coefficient of variation (CV)0.53897882
Kurtosis-1.2239057
Mean6.4269006
Median Absolute Deviation (MAD)3
Skewness0.0098475028
Sum7693
Variance11.999042
MonotonicityNot monotonic
2025-10-24T12:22:53.306141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8109
9.1%
2109
9.1%
1105
8.8%
4105
8.8%
9104
8.7%
10100
8.4%
1299
8.3%
796
8.0%
395
7.9%
694
7.9%
Other values (2)181
15.1%
ValueCountFrequency (%)
1105
8.8%
2109
9.1%
395
7.9%
4105
8.8%
593
7.8%
694
7.9%
796
8.0%
8109
9.1%
9104
8.7%
10100
8.4%
ValueCountFrequency (%)
1299
8.3%
1188
7.4%
10100
8.4%
9104
8.7%
8109
9.1%
796
8.0%
694
7.9%
593
7.8%
4105
8.8%
395
7.9%

targeted_productivity
Real number (ℝ)

Distinct9
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.72963241
Minimum0.07
Maximum0.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-10-24T12:22:53.416066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.5
Q10.7
median0.75
Q30.8
95-th percentile0.8
Maximum0.8
Range0.73
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.097890963
Coefficient of variation (CV)0.13416477
Kurtosis5.6137006
Mean0.72963241
Median Absolute Deviation (MAD)0.05
Skewness-2.14415
Sum873.37
Variance0.0095826407
MonotonicityNot monotonic
2025-10-24T12:22:53.537907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.8540
45.1%
0.7242
20.2%
0.75216
 
18.0%
0.6563
 
5.3%
0.657
 
4.8%
0.549
 
4.1%
0.3527
 
2.3%
0.42
 
0.2%
0.071
 
0.1%
ValueCountFrequency (%)
0.071
 
0.1%
0.3527
 
2.3%
0.42
 
0.2%
0.549
 
4.1%
0.657
 
4.8%
0.6563
 
5.3%
0.7242
20.2%
0.75216
 
18.0%
0.8540
45.1%
ValueCountFrequency (%)
0.8540
45.1%
0.75216
 
18.0%
0.7242
20.2%
0.6563
 
5.3%
0.657
 
4.8%
0.549
 
4.1%
0.42
 
0.2%
0.3527
 
2.3%
0.071
 
0.1%

smv
Real number (ℝ)

High correlation 

Distinct70
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.062172
Minimum2.9
Maximum54.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-10-24T12:22:53.690425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile2.9
Q13.94
median15.26
Q324.26
95-th percentile30.1
Maximum54.56
Range51.66
Interquartile range (IQR)20.32

Descriptive statistics

Standard deviation10.943219
Coefficient of variation (CV)0.72653659
Kurtosis-0.79534591
Mean15.062172
Median Absolute Deviation (MAD)11.11
Skewness0.40593674
Sum18029.42
Variance119.75405
MonotonicityNot monotonic
2025-10-24T12:22:53.853407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.94192
16.0%
2.9108
 
9.0%
22.52103
 
8.6%
30.179
 
6.6%
4.1576
 
6.3%
18.7950
 
4.2%
4.646
 
3.8%
15.2644
 
3.7%
25.932
 
2.7%
11.6131
 
2.6%
Other values (60)436
36.4%
ValueCountFrequency (%)
2.9108
9.0%
3.920
 
1.7%
3.94192
16.0%
4.0821
 
1.8%
4.1576
 
6.3%
4.317
 
1.4%
4.646
 
3.8%
5.1326
 
2.2%
10.056
 
0.5%
11.4130
 
2.5%
ValueCountFrequency (%)
54.561
0.1%
51.021
0.1%
50.891
0.1%
50.482
0.2%
49.11
0.1%
48.842
0.2%
48.681
0.1%
48.181
0.1%
45.671
0.1%
42.972
0.2%

wip
Real number (ℝ)

High correlation  Missing 

Distinct548
Distinct (%)79.3%
Missing506
Missing (%)42.3%
Infinite0
Infinite (%)0.0%
Mean1190.466
Minimum7
Maximum23122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-10-24T12:22:54.216036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile358.5
Q1774.5
median1039
Q31252.5
95-th percentile1602
Maximum23122
Range23115
Interquartile range (IQR)478

Descriptive statistics

Standard deviation1837.455
Coefficient of variation (CV)1.5434754
Kurtosis101.70204
Mean1190.466
Median Absolute Deviation (MAD)232
Skewness9.7417863
Sum822612
Variance3376240.9
MonotonicityNot monotonic
2025-10-24T12:22:54.382649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10395
 
0.4%
12824
 
0.3%
14223
 
0.3%
12163
 
0.3%
14133
 
0.3%
14483
 
0.3%
7593
 
0.3%
10953
 
0.3%
11083
 
0.3%
10793
 
0.3%
Other values (538)658
55.0%
(Missing)506
42.3%
ValueCountFrequency (%)
71
0.1%
101
0.1%
111
0.1%
121
0.1%
131
0.1%
141
0.1%
151
0.1%
291
0.1%
301
0.1%
521
0.1%
ValueCountFrequency (%)
231221
0.1%
215401
0.1%
213851
0.1%
212661
0.1%
168821
0.1%
122611
0.1%
97921
0.1%
89921
0.1%
29841
0.1%
26981
0.1%

over_time
Real number (ℝ)

High correlation  Zeros 

Distinct143
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4567.4603
Minimum0
Maximum25920
Zeros31
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-10-24T12:22:54.535967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile960
Q11440
median3960
Q36960
95-th percentile10368
Maximum25920
Range25920
Interquartile range (IQR)5520

Descriptive statistics

Standard deviation3348.8236
Coefficient of variation (CV)0.73319161
Kurtosis0.4243643
Mean4567.4603
Median Absolute Deviation (MAD)2760
Skewness0.6732873
Sum5467250
Variance11214619
MonotonicityNot monotonic
2025-10-24T12:22:54.686444image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
960129
 
10.8%
1440111
 
9.3%
696061
 
5.1%
684048
 
4.0%
120039
 
3.3%
180038
 
3.2%
1017036
 
3.0%
031
 
2.6%
336030
 
2.5%
408030
 
2.5%
Other values (133)644
53.8%
ValueCountFrequency (%)
031
 
2.6%
1201
 
0.1%
2406
 
0.5%
3602
 
0.2%
4801
 
0.1%
6004
 
0.3%
7204
 
0.3%
8402
 
0.2%
9002
 
0.2%
960129
10.8%
ValueCountFrequency (%)
259201
 
0.1%
151201
 
0.1%
150002
 
0.2%
146401
 
0.1%
138001
 
0.1%
126001
 
0.1%
121801
 
0.1%
120001
 
0.1%
107701
 
0.1%
1062022
1.8%

incentive
Real number (ℝ)

High correlation  Zeros 

Distinct48
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.210526
Minimum0
Maximum3600
Zeros604
Zeros (%)50.5%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-10-24T12:22:54.836596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q350
95-th percentile88
Maximum3600
Range3600
Interquartile range (IQR)50

Descriptive statistics

Standard deviation160.18264
Coefficient of variation (CV)4.1921077
Kurtosis299.03246
Mean38.210526
Median Absolute Deviation (MAD)0
Skewness15.790746
Sum45738
Variance25658.479
MonotonicityNot monotonic
2025-10-24T12:22:54.986643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0604
50.5%
50113
 
9.4%
6361
 
5.1%
4554
 
4.5%
3052
 
4.3%
2338
 
3.2%
3829
 
2.4%
6028
 
2.3%
4027
 
2.3%
7524
 
2.0%
Other values (38)167
 
14.0%
ValueCountFrequency (%)
0604
50.5%
211
 
0.1%
2338
 
3.2%
242
 
0.2%
251
 
0.1%
269
 
0.8%
272
 
0.2%
291
 
0.1%
3052
 
4.3%
321
 
0.1%
ValueCountFrequency (%)
36001
 
0.1%
28801
 
0.1%
14401
 
0.1%
12001
 
0.1%
10801
 
0.1%
9605
 
0.4%
1381
 
0.1%
1192
 
0.2%
11321
1.8%
1007
 
0.6%

idle_time
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct12
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.73015873
Minimum0
Maximum300
Zeros1179
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-10-24T12:22:55.128854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum300
Range300
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.709757
Coefficient of variation (CV)17.40684
Kurtosis442.63816
Mean0.73015873
Median Absolute Deviation (MAD)0
Skewness20.545425
Sum874
Variance161.53791
MonotonicityNot monotonic
2025-10-24T12:22:55.236846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
01179
98.5%
3.53
 
0.3%
22
 
0.2%
52
 
0.2%
82
 
0.2%
4.52
 
0.2%
42
 
0.2%
901
 
0.1%
1501
 
0.1%
2701
 
0.1%
Other values (2)2
 
0.2%
ValueCountFrequency (%)
01179
98.5%
22
 
0.2%
3.53
 
0.3%
42
 
0.2%
4.52
 
0.2%
52
 
0.2%
6.51
 
0.1%
82
 
0.2%
901
 
0.1%
1501
 
0.1%
ValueCountFrequency (%)
3001
 
0.1%
2701
 
0.1%
1501
 
0.1%
901
 
0.1%
82
0.2%
6.51
 
0.1%
52
0.2%
4.52
0.2%
42
0.2%
3.53
0.3%

idle_men
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36925647
Minimum0
Maximum45
Zeros1179
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-10-24T12:22:55.371569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum45
Range45
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.2689873
Coefficient of variation (CV)8.852891
Kurtosis102.96287
Mean0.36925647
Median Absolute Deviation (MAD)0
Skewness9.8550791
Sum442
Variance10.686278
MonotonicityNot monotonic
2025-10-24T12:22:55.484382image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
01179
98.5%
103
 
0.3%
153
 
0.3%
303
 
0.3%
203
 
0.3%
352
 
0.2%
451
 
0.1%
371
 
0.1%
251
 
0.1%
401
 
0.1%
ValueCountFrequency (%)
01179
98.5%
103
 
0.3%
153
 
0.3%
203
 
0.3%
251
 
0.1%
303
 
0.3%
352
 
0.2%
371
 
0.1%
401
 
0.1%
451
 
0.1%
ValueCountFrequency (%)
451
 
0.1%
401
 
0.1%
371
 
0.1%
352
 
0.2%
303
 
0.3%
251
 
0.1%
203
 
0.3%
153
 
0.3%
103
 
0.3%
01179
98.5%

no_of_style_change
Categorical

Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
0
1050 
1
114 
2
 
33

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1197
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01050
87.7%
1114
 
9.5%
233
 
2.8%

Length

2025-10-24T12:22:55.609133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-24T12:22:55.713022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
01050
87.7%
1114
 
9.5%
233
 
2.8%

Most occurring characters

ValueCountFrequency (%)
01050
87.7%
1114
 
9.5%
233
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1197
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01050
87.7%
1114
 
9.5%
233
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1197
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01050
87.7%
1114
 
9.5%
233
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1197
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01050
87.7%
1114
 
9.5%
233
 
2.8%

no_of_workers
Real number (ℝ)

High correlation 

Distinct61
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.609858
Minimum2
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-10-24T12:22:55.840894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q19
median34
Q357
95-th percentile59
Maximum89
Range87
Interquartile range (IQR)48

Descriptive statistics

Standard deviation22.197687
Coefficient of variation (CV)0.6413689
Kurtosis-1.7881079
Mean34.609858
Median Absolute Deviation (MAD)24
Skewness-0.11173973
Sum41428
Variance492.73729
MonotonicityNot monotonic
2025-10-24T12:22:55.992188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8262
21.9%
58114
 
9.5%
57109
 
9.1%
5975
 
6.3%
1060
 
5.0%
56.554
 
4.5%
5649
 
4.1%
3443
 
3.6%
942
 
3.5%
1237
 
3.1%
Other values (51)352
29.4%
ValueCountFrequency (%)
26
 
0.5%
41
 
0.1%
53
 
0.3%
61
 
0.1%
73
 
0.3%
8262
21.9%
942
 
3.5%
1060
 
5.0%
111
 
0.1%
1237
 
3.1%
ValueCountFrequency (%)
891
 
0.1%
607
 
0.6%
59.55
 
0.4%
5975
6.3%
58.521
 
1.8%
58114
9.5%
57.525
 
2.1%
57109
9.1%
56.554
4.5%
5649
4.1%

actual_productivity
Real number (ℝ)

Distinct879
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7350911
Minimum0.23370548
Maximum1.1204375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-10-24T12:22:56.144029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.23370548
5-th percentile0.35551319
Q10.65030714
median0.77333333
Q30.85025253
95-th percentile0.97703788
Maximum1.1204375
Range0.88673202
Interquartile range (IQR)0.19994538

Descriptive statistics

Standard deviation0.1744879
Coefficient of variation (CV)0.23736909
Kurtosis0.33322734
Mean0.7350911
Median Absolute Deviation (MAD)0.090833333
Skewness-0.80749177
Sum879.90404
Variance0.030446028
MonotonicityNot monotonic
2025-10-24T12:22:56.296900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.80040196124
 
2.0%
0.97186666712
 
1.0%
0.85013676612
 
1.0%
0.7506510111
 
0.9%
0.85050231111
 
0.9%
1.00023040911
 
0.9%
0.8001287218
 
0.7%
0.7503955138
 
0.7%
0.8581439397
 
0.6%
0.8001171037
 
0.6%
Other values (869)1086
90.7%
ValueCountFrequency (%)
0.2337054761
0.1%
0.2357954551
0.1%
0.2380416671
0.1%
0.246251
0.1%
0.2473160171
0.1%
0.2494166671
0.1%
0.2513992541
0.1%
0.25651
0.1%
0.2581
0.1%
0.2593751
0.1%
ValueCountFrequency (%)
1.12043751
0.1%
1.1081251
0.1%
1.1004839181
0.1%
1.0966333331
0.1%
1.0596212121
0.1%
1.0579629631
0.1%
1.0506666671
0.1%
1.050280581
0.1%
1.0335700761
0.1%
1.0331555561
0.1%

Interactions

2025-10-24T12:22:50.314532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:40.130753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:41.274017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:42.528874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:43.571965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:44.622654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:45.838986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:46.961876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:48.051263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:49.257598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:50.448379image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:40.258373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:41.507857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:42.629365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:43.674693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:44.862192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:45.965676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:47.067033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:48.155994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:49.360989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:50.566339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:40.373498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:41.626094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:42.743028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:43.790153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:44.985790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:46.085889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:47.181406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:48.271155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:49.476858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:50.669933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:40.473639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:41.737715image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:42.837823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:43.890480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:45.087924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:46.192245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:47.286294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:48.368448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:49.578244image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:50.772451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:40.583276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:41.843582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:42.938497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:43.988321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:45.187708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:46.296753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:47.388480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:48.470691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:49.681000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:50.879341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:40.688814image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:41.958064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:43.044740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:44.091562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:45.290544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:46.412447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:47.497487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:48.574049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:49.787533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:50.992646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:40.802436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:42.080986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:43.161365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:44.205468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:45.408789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:46.530919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:47.612980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:48.849854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:49.896398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:51.100441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:40.912721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:42.197289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:43.269841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:44.314651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:45.520736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:46.642528image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:47.724996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:48.958591image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:50.015727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:51.203236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:41.054992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:42.310525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:43.368099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:44.416718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:45.622551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:46.748577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:47.833059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:49.057280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:50.115927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:51.305140image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:41.172659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:42.417356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:43.469009image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:44.523486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:45.732973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:46.855405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:47.943162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:49.156852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-24T12:22:50.214703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-10-24T12:22:56.411515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
actual_productivitydaydepartmentidle_menidle_timeincentiveno_of_style_changeno_of_workersover_timequartersmvtargeted_productivityteamwip
actual_productivity1.0000.0310.315-0.148-0.1480.2170.188-0.035-0.0760.131-0.1220.448-0.1640.233
day0.0311.0000.0000.0000.0080.0800.0000.0000.0250.1390.0000.0450.0000.077
department0.3150.0001.0000.0000.0000.1130.2230.7020.5460.0960.7020.0730.0001.000
idle_men-0.1480.0000.0001.0001.000-0.0540.1800.135-0.0180.0320.137-0.0590.022-0.193
idle_time-0.1480.0080.0001.0001.000-0.0540.0000.135-0.0180.0000.137-0.0590.022-0.193
incentive0.2170.0800.113-0.054-0.0541.0000.0000.6570.5390.0460.6010.202-0.0170.302
no_of_style_change0.1880.0000.2230.1800.0000.0001.0000.3520.2160.1880.4240.1870.1530.000
no_of_workers-0.0350.0000.7020.1350.1350.6570.3521.0000.7440.0320.891-0.048-0.124-0.059
over_time-0.0760.0250.546-0.018-0.0180.5390.2160.7441.0000.1250.700-0.072-0.1080.137
quarter0.1310.1390.0960.0320.0000.0460.1880.0320.1251.0000.1290.1010.0000.000
smv-0.1220.0000.7020.1370.1370.6010.4240.8910.7000.1291.000-0.091-0.112-0.143
targeted_productivity0.4480.0450.073-0.059-0.0590.2020.187-0.048-0.0720.101-0.0911.0000.037-0.013
team-0.1640.0000.0000.0220.022-0.0170.153-0.124-0.1080.000-0.1120.0371.000-0.035
wip0.2330.0771.000-0.193-0.1930.3020.000-0.0590.1370.000-0.143-0.013-0.0351.000

Missing values

2025-10-24T12:22:51.463822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-24T12:22:51.726746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

datequarterdepartmentdayteamtargeted_productivitysmvwipover_timeincentiveidle_timeidle_menno_of_style_changeno_of_workersactual_productivity
01/1/2015Quarter1sweingThursday80.8026.161108.07080980.00059.00.940725
11/1/2015Quarter1finishingThursday10.753.94NaN96000.0008.00.886500
21/1/2015Quarter1sweingThursday110.8011.41968.03660500.00030.50.800570
31/1/2015Quarter1sweingThursday120.8011.41968.03660500.00030.50.800570
41/1/2015Quarter1sweingThursday60.8025.901170.01920500.00056.00.800382
51/1/2015Quarter1sweingThursday70.8025.90984.06720380.00056.00.800125
61/1/2015Quarter1finishingThursday20.753.94NaN96000.0008.00.755167
71/1/2015Quarter1sweingThursday30.7528.08795.06900450.00057.50.753683
81/1/2015Quarter1sweingThursday20.7519.87733.06000340.00055.00.753098
91/1/2015Quarter1sweingThursday10.7528.08681.06900450.00057.50.750428
datequarterdepartmentdayteamtargeted_productivitysmvwipover_timeincentiveidle_timeidle_menno_of_style_changeno_of_workersactual_productivity
11873/11/2015Quarter2sweingWednesday40.7526.821054.07080450.00059.00.750051
11883/11/2015Quarter2sweingWednesday50.7026.82992.06960300.00158.00.700557
11893/11/2015Quarter2sweingWednesday80.7030.48914.06840300.00157.00.700505
11903/11/2015Quarter2sweingWednesday60.7023.411128.04560400.00138.00.700246
11913/11/2015Quarter2sweingWednesday70.6530.48935.06840260.00157.00.650596
11923/11/2015Quarter2finishingWednesday100.752.90NaN96000.0008.00.628333
11933/11/2015Quarter2finishingWednesday80.703.90NaN96000.0008.00.625625
11943/11/2015Quarter2finishingWednesday70.653.90NaN96000.0008.00.625625
11953/11/2015Quarter2finishingWednesday90.752.90NaN180000.00015.00.505889
11963/11/2015Quarter2finishingWednesday60.702.90NaN72000.0006.00.394722